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OpenCV2.4.4中调用SIFT特征检测器进行图像匹配
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OpenCV中一些相關結構說明：
特征點類：
[cpp]?view plain
?copy ? class?KeyPoint?? {?? ???????Point2f??pt;???? ???????float??size;??? ???????float??angle;??? ???????float??response;??? ???????int??octave;??? ???????int??class_id;??? }??
存放匹配結果的結構：
[cpp]?view plain
?copy ? struct?DMatch?? {?? ????????? ????DMatch():?queryIdx(-1),?trainIdx(-1),imgIdx(-1),distance(std::numeric_limits<float>::max())?{}?? ????DMatch(int??_queryIdx,?int??_trainIdx,?float??_distance?)?:?? ?????????????????????queryIdx(?_queryIdx),trainIdx(?_trainIdx),?imgIdx(-1),distance(?_distance)?{}?? ????DMatch(int??_queryIdx,?int??_trainIdx,?int??_imgIdx,?float??_distance?)?:?? ????????????queryIdx(_queryIdx),?trainIdx(?_trainIdx),?imgIdx(?_imgIdx),distance(?_distance)?{}?? ?? ????intqueryIdx;???? ????inttrainIdx;????? ????intimgIdx;?????? ????float?distance;???? ????booloperator?<?(const?DMatch?&m)?const;?? };??
?????? 說明：以兩個特征點描述子(特征向量)之間的歐氏距離作為特征點匹配的相似度準則，假設特征點對p和q的
????????????? ?特征描述子分別為Desp和Desq，則其歐氏距離定義為：
????????????? ? 所以每個匹配分別對應訓練圖像(train)和查詢圖像(query)中的一個特征描述子(特征向量)。
[cpp]?view plain
?copy ? #include?"opencv2/highgui/highgui.hpp"?? #include?"opencv2/imgproc/imgproc.hpp"?? #include?"opencv2/nonfree/nonfree.hpp"?? #include?"opencv2/nonfree/features2d.hpp"?? #include?<iostream>?? #include?<stdio.h>?? #include?<stdlib.h>?? ?? using?namespace?cv;?? using?namespace?std;?? ?? int?main()?? {?? ????initModule_nonfree();?? ????Ptr<FeatureDetector>?detector?=?FeatureDetector::create(?"SIFT"?);?? ????Ptr<DescriptorExtractor>?descriptor_extractor?=?DescriptorExtractor::create(?"SIFT"?);?? ????Ptr<DescriptorMatcher>?descriptor_matcher?=?DescriptorMatcher::create(?"BruteForce"?);?? ????if(?detector.empty()?||?descriptor_extractor.empty()?)?? ????????cout<<"fail?to?create?detector!";?? ?? ?????? ????Mat?img1?=?imread("desk.jpg");?? ????Mat?img2?=?imread("desk_glue.jpg");?? ?? ?????? ????double?t?=?getTickCount();?? ????vector<KeyPoint>?keypoints1,keypoints2;?? ????detector->detect(?img1,?keypoints1?);?? ????detector->detect(?img2,?keypoints2?);?? ????cout<<"圖像1特征點個數:"<<keypoints1.size()<<endl;?? ????cout<<"圖像2特征點個數:"<<keypoints2.size()<<endl;?? ?? ?????? ????Mat?descriptors1,descriptors2;?? ????descriptor_extractor->compute(?img1,?keypoints1,?descriptors1?);?? ????descriptor_extractor->compute(?img2,?keypoints2,?descriptors2?);?? ????t?=?((double)getTickCount()?-?t)/getTickFrequency();?? ????cout<<"SIFT算法用時："<<t<<"秒"<<endl;?? ?? ?? ????cout<<"圖像1特征描述矩陣大小："<<descriptors1.size()?? ????????<<"，特征向量個數："<<descriptors1.rows<<"，維數："<<descriptors1.cols<<endl;?? ????cout<<"圖像2特征描述矩陣大小："<<descriptors2.size()?? ????????<<"，特征向量個數："<<descriptors2.rows<<"，維數："<<descriptors2.cols<<endl;?? ?? ?????? ????Mat?img_keypoints1,img_keypoints2;?? ????drawKeypoints(img1,keypoints1,img_keypoints1,Scalar::all(-1),0);?? ????drawKeypoints(img2,keypoints2,img_keypoints2,Scalar::all(-1),0);?? ?????? ?????? ?? ?????? ????vector<DMatch>?matches;?? ????descriptor_matcher->match(?descriptors1,?descriptors2,?matches?);?? ????cout<<"Match個數："<<matches.size()<<endl;?? ?? ?????? ?????? ????double?max_dist?=?0;?? ????double?min_dist?=?100;?? ????for(int?i=0;?i<matches.size();?i++)?? ????{?? ????????double?dist?=?matches[i].distance;?? ????????if(dist?<?min_dist)?min_dist?=?dist;?? ????????if(dist?>?max_dist)?max_dist?=?dist;?? ????}?? ????cout<<"最大距離："<<max_dist<<endl;?? ????cout<<"最小距離："<<min_dist<<endl;?? ?? ?????? ????vector<DMatch>?goodMatches;?? ????for(int?i=0;?i<matches.size();?i++)?? ????{?? ????????if(matches[i].distance?<?0.31?*?max_dist)?? ????????{?? ????????????goodMatches.push_back(matches[i]);?? ????????}?? ????}?? ????cout<<"goodMatch個數："<<goodMatches.size()<<endl;?? ?? ?????? ????Mat?img_matches;?? ?????? ????drawMatches(img1,keypoints1,img2,keypoints2,goodMatches,img_matches,?? ????????????????Scalar::all(-1),CV_RGB(0,255,0),Mat(),2);?? ?? ????imshow("MatchSIFT",img_matches);?? ????waitKey(0);?? ????return?0;?? }??
結果：
效果圖：
源碼下載：
http://download.csdn.net/detail/masikkk/5511831
當然，這些匹配還沒有經過系統的篩選，還存在大量的錯配，關于匹配的篩選參見這篇文章：
?利用RANSAC算法篩選SIFT特征匹配
以及RobHess的SIFT源碼分析系列文章：http://blog.csdn.net/masibuaa/article/details/9191309
from:?http://blog.csdn.net/masibuaa/article/details/8998601
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